Abstract

Asthma is a chronic respiratory disease that affects breathing and may also influence speech and voice production. In this paper, we examine whether short mobile-recorded Brazilian Portuguese voice and speech audio contain cues that can be used to distinguish individuals with asthma from those without asthma. We approach this problem using transfer learning with pretrained neural audio models based on convolutional architectures trained on large-scale audio datasets (PANNs). We evaluate two recording types: sustained vowel phonation and read speech. Models are trained for a binary classification task and evaluated at both the segment level and the patient level. Read speech performs better than sustained vowels. The best configuration (CNN14 on speech) achieves 0.85 patient-level balanced accuracy (accuracy 0.85) with ROC-AUC 0.93 and PR-AUC 0.98, performing comparably to CNN10. Training from scratch performs worse than fine-tuning a pretrained model, showing that pretraining helps when data is limited. Performance also varies across age groups, suggesting demographic sensitivity. These findings support the feasibility of audio-based asthma classification from voice and speech and motivate further investigation of pretrained audio models in biomedical applications.